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Section: New Results
Audio-Source Separation
We address the problem of separating audio sources from time-varying convolutive mixtures. We proposed an unsupervised probabilistic framework based on the local complex-Gaussian model combined with non-negative matrix factorization [33], [28]. The time-varying mixing filters are modeled by a continuous temporal stochastic process. This model extends the case of static filters which corresponds to static audio sources. While static filters can be learnt in advance, e.g. [5], time-varying filters cannot and therefore the problem is more complex. We present a variational expectation-maximization (VEM) algorithm that employs a Kalman smoother to estimate the time-varying mixing matrix, and that jointly estimates the source parameters. The sound sources are then separated by Wiener filters constructed with the estimators provided by the VEM algorithm. Extensive experiments on simulated data show that the proposed method outperforms a block-wise version of a state-of-the-art baseline method. This work is part of the PhD topic of Dionyssos Kounades Bastian and is conducted in collaboration with Sharon Gannot (Bar Ilan University) and Xavier Alameda Pineda (University of Trento). Our journal paper [28] is an extended version of a paper presented at IEEE WASPAA in 2015 which received the best student paper award.
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